In practical applications, many 3D models consist of a large number of connected components. These multi-component 3D models usually contain many repetitive structures in various transformations, as shown in FIG. 1.
Efficient compression algorithms for multi-component 3D models that take advantage of repetitive structures in input models are known. Repetitive structures of a 3D model are discovered in various positions, orientations, and scaling factors. The 3D model is then organized into “pattern-instance” representation. A pattern is used to denote a representative geometry of a corresponding repetitive structure. Components belonging to a repetitive structure are denoted as instances of the corresponding pattern and may be represented by a pattern ID and transformation information, for example, reflection, translation, rotation and possible scaling with respect to the pattern. The instance transformation information may be organized into, for example, reflection part, translation part, rotation part, and possible scaling part. There might be some components of the 3D models that are not repetitive, which are referred to as unique components.
A commonly owned PCT application, entitled “System and method for error controllable repetitive structure discovery based compression” by K. Cai, W. Jiang, and T. Luo (PCT/CN2012/070877), the teachings of which are specifically incorporated herein by reference, discloses a method and apparatus for identifying repetitive structures in 3D models to reduce redundancy among instance components, and thus to improve compression efficiency.